Class-Balanced Loss Based on Effective Number of Samples

16 January 2019

Papers citing "Class-Balanced Loss Based on Effective Number of Samples"

9 / 1,009 papers shown

Title
Data Augmentation for Object Detection via Progressive and Selective Instance-Switching Hao Wang Qilong Wang Fan Yang Jiexia Ye W. Zuo 16 62 0 02 Jun 2019
Differential Privacy Has Disparate Impact on Model Accuracy Eugene Bagdasaryan Vitaly Shmatikov 10 467 0 28 May 2019
Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework Zhenmao Li Yichao Wu Ken Chen Yudong Wu Shunfeng Zhou Jiaheng Liu Junjie Yan 18 5 0 27 May 2019
SelectNet: Learning to Sample from the Wild for Imbalanced Data Training Yunru Liu Tingran Gao Haizhao Yang 11 12 0 23 May 2019
Synthetic Examples Improve Generalization for Rare Classes Sara Beery Yang Liu Dan Morris Jim Piavis Ashish Kapoor M. Meister Neel Joshi Pietro Perona 26 95 0 11 Apr 2019
Striking the Right Balance with Uncertainty Salman Khan Munawar Hayat Waqas Zamir Jianbing Shen Ling Shao 25 174 0 22 Jan 2019
Dynamic Curriculum Learning for Imbalanced Data Classification Yiru Wang Weihao Gan Jie Yang Wei Wu Junjie Yan 25 217 0 21 Jan 2019
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild Lianghua Huang Xin Zhao Kaiqi Huang 51 1,325 0 29 Oct 2018
Range Loss for Deep Face Recognition with Long-tail Xiao Zhang Zhiyuan Fang Yandong Wen Zhifeng Li Yu Qiao CVBM 243 447 0 28 Nov 2016